A deep reinforcement learning-guided multimodal multi-objective evolutionary algorithm with a serial-parallel mechanism

The core challenge for multimodal multi-objective problem (MMOP) resolution lies in maintaining synergistic interactions between convergence and diversity. However, the existing algorithms usually consider convergence-first, which neglect to consider both diversity and convergence into account during the evolutionary process. Likewise, the optimization methods tend to gravitate toward locally optimal regions rapidly, leading to lose diversity for the local PS. This paper proposes a Deep Reinforcement Learning-guided multimodal multi-objective evolutionary algorithm with a serial-parallel mechanism (DRLMMEA) to investigate the impact of different operator selection on the performance of MMEAs, which greatly helps to balance the convergence and diversity. DRLMMEA utilizes Q-Network to select the operator with the highest reward to enhance the population’s search ability. An improved sorting method (ISM) based on neighborhood dominance updates the population by sorting individuals according to their convergence quality, thereby enhancing convergence performance in the objective space. Moreover, this study proposes a series-parallel mechanism, a series structure enhances the diversity in the decision space, while the parallel structure reduces the computational burden of the algorithm evidently. The proposed Deep Reinforcement Learning-assisted operator selection mechanism, which enables effective balance between diversity and convergence, and an improved crowding distance approach that enhances convergence performance. DRLMMEA undergoes comprehensive testing against 6 contemporary approaches using MMF and IDMP benchmark problems, achieving supremacy in 4 principal performance metrics according to experimental findings. The multimodal gearbox parameter optimization is addressed using the proposed DRLMMEA, which demonstrates superior performance against 6 algorithms in comparative evaluations. It has demonstrated a significant role in solving the MMOPs with the imbalance between convergence and diversity.